{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "root = '/store2/dataset/coco'\n",
    "\n",
    "subfolders = ['annotations']\n",
    "\n",
    "anno_root = os.path.join(root, 'annotations')\n",
    "\n",
    "anno_path = os.path.join(anno_root, 'instances_train2014.json')\n",
    "\n",
    "import json\n",
    "train = json.load(open(anno_path, 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2], device='cuda:0')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.tensor([1,2]).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'segmentation': [[161.55, 381.18, 153.11, 371.68, 147.83, 246.03, 156.28, 237.58, 175.28, 251.31, 175.28, 261.87, 192.18, 270.31, 201.68, 269.26, 213.29, 258.7, 219.63, 247.08, 227.02, 247.08, 241.8, 241.8, 257.64, 244.97, 263.98, 252.36, 261.87, 376.96, 244.97, 393.86, 211.18, 386.46, 200.62, 386.46, 180.56, 398.08]], 'area': 15239.296649999998, 'iscrowd': 0, 'image_id': 495077, 'bbox': [147.83, 237.58, 116.15, 160.5], 'category_id': 58, 'id': 1282}\n",
      "{'segmentation': [[209.62, 419.61, 229.78, 410.42, 233.63, 409.83, 237.19, 411.31, 236.0, 416.65, 223.26, 421.39]], 'area': 159.2581499999994, 'iscrowd': 0, 'image_id': 205955, 'bbox': [209.62, 409.83, 27.57, 11.56], 'category_id': 58, 'id': 1285}\n",
      "{'segmentation': [[228.27, 184.36, 230.54, 175.93, 221.49, 168.53, 237.32, 156.19, 245.96, 158.04, 258.3, 154.96, 262.41, 156.81, 263.03, 157.22, 265.7, 159.48, 269.81, 158.04, 271.67, 158.25, 279.07, 161.74, 293.88, 169.97, 285.65, 183.75, 282.36, 186.42, 278.45, 182.51, 272.49, 184.16, 271.87, 187.66, 271.46, 195.47, 264.67, 197.94]], 'area': 1908.8649499999997, 'iscrowd': 0, 'image_id': 86064, 'bbox': [221.49, 154.96, 72.39, 42.98], 'category_id': 58, 'id': 1289}\n",
      "{'segmentation': [[57.47, 412.41, 37.4, 380.48, 34.66, 358.58, 45.61, 334.87, 58.38, 313.89, 70.24, 304.76, 108.55, 362.23, 99.43, 367.71, 76.63, 385.95, 74.8, 385.95, 72.06, 405.11, 69.33, 413.32, 59.29, 415.14]], 'area': 4324.7918, 'iscrowd': 0, 'image_id': 66877, 'bbox': [34.66, 304.76, 73.89, 110.38], 'category_id': 58, 'id': 1293}\n",
      "{'segmentation': [[432.62, 285.46, 436.43, 289.28, 438.62, 290.1, 441.07, 289.83, 444.89, 292.01, 450.35, 291.19, 454.44, 286.83, 455.53, 285.74, 460.17, 282.73, 462.08, 279.19, 460.71, 268.0, 456.89, 260.64, 450.35, 252.73, 444.62, 249.18, 439.16, 248.09, 435.07, 246.73, 430.43, 245.91, 426.34, 246.18, 425.8, 248.36, 427.16, 251.09, 427.43, 252.73, 424.98, 252.73, 423.34, 253.55, 421.98, 256.27, 424.16, 257.09, 425.8, 262.0, 432.07, 284.64, 432.62, 285.46]], 'area': 1221.6388499999994, 'iscrowd': 0, 'image_id': 167683, 'bbox': [421.98, 245.91, 40.1, 46.1], 'category_id': 58, 'id': 1308}\n",
      "{'segmentation': [[433.72, 271.53, 425.03, 266.45, 422.13, 263.19, 414.89, 265.37, 408.37, 266.45, 403.3, 263.56, 397.86, 258.12, 396.78, 249.07, 398.95, 245.44, 401.49, 240.74, 402.57, 238.56, 410.54, 231.68, 409.82, 226.61, 419.24, 219.73, 421.05, 219.36, 425.39, 213.21, 426.84, 213.21, 430.1, 213.57, 429.74, 216.47, 430.83, 216.83, 432.64, 214.29, 434.81, 213.21, 436.62, 214.66, 438.43, 212.12, 440.61, 211.4, 446.04, 208.5, 446.04, 207.05, 449.66, 206.32, 455.82, 201.98, 457.63, 200.53, 460.53, 200.53, 461.62, 203.43, 471.03, 196.54, 476.83, 194.37, 484.44, 196.54, 485.16, 194.01, 486.97, 193.28, 497.48, 196.54, 500.37, 200.53, 504.36, 211.76, 504.72, 213.93, 486.97, 229.51, 468.86, 244.72, 456.18, 257.76, 450.75, 266.45, 446.76, 269.71, 438.43, 271.16]], 'area': 4588.210900000001, 'iscrowd': 0, 'image_id': 339295, 'bbox': [396.78, 193.28, 107.94, 78.25], 'category_id': 58, 'id': 1309}\n",
      "{'segmentation': [[434.26, 290.65, 434.88, 286.0, 440.15, 280.11, 443.87, 265.53, 445.42, 263.36, 451.0, 262.43, 455.96, 264.6, 458.14, 269.25, 460.93, 274.84, 460.31, 286.93, 457.52, 299.33, 453.48, 308.95, 449.45, 314.53, 447.9, 314.84, 444.18, 305.53, 439.22, 298.09, 437.05, 294.06]], 'area': 863.6678999999995, 'iscrowd': 0, 'image_id': 580930, 'bbox': [434.26, 262.43, 26.67, 52.41], 'category_id': 58, 'id': 1324}\n",
      "{'segmentation': [[114.53, 402.95, 108.29, 394.62, 113.49, 382.13, 131.19, 354.01, 150.98, 344.64, 152.02, 337.35, 164.51, 335.27, 172.84, 336.31, 170.76, 345.68, 154.1, 361.3, 137.44, 381.08, 127.03, 401.91, 117.66, 407.11]], 'area': 1604.5142000000008, 'iscrowd': 0, 'image_id': 528892, 'bbox': [108.29, 335.27, 64.55, 71.84], 'category_id': 58, 'id': 1341}\n",
      "{'segmentation': [[292.85, 108.51, 449.41, 137.86, 455.7, 153.24, 446.61, 165.82, 436.83, 163.72, 430.54, 147.65, 424.95, 144.15, 364.14, 130.17, 293.55, 114.8, 273.28, 116.2]], 'area': 1784.3782499999975, 'iscrowd': 0, 'image_id': 99788, 'bbox': [273.28, 108.51, 182.42, 57.31], 'category_id': 58, 'id': 1378}\n",
      "{'segmentation': [[318.06, 366.73, 258.66, 372.22, 254.09, 383.19, 303.44, 390.5, 360.11, 386.84, 367.42, 383.19, 363.76, 372.22, 352.8, 367.65]], 'area': 2065.1939499999976, 'iscrowd': 0, 'image_id': 568098, 'bbox': [254.09, 366.73, 113.33, 23.77], 'category_id': 58, 'id': 1387}\n",
      "{'segmentation': [[381.25, 483.44, 334.45, 515.1, 319.31, 516.47, 352.34, 532.99, 403.27, 505.46, 403.27, 493.08]], 'area': 1849.6550499999992, 'iscrowd': 0, 'image_id': 97914, 'bbox': [319.31, 483.44, 83.96, 49.55], 'category_id': 58, 'id': 1397}\n",
      "{'segmentation': [[430.02, 182.21, 413.36, 165.55, 403.99, 145.77, 403.99, 122.86, 417.53, 103.08, 435.23, 99.96, 455.01, 102.04, 468.55, 115.57, 475.84, 127.03, 484.16, 148.89, 487.29, 157.22, 483.12, 164.51, 472.71, 174.92, 439.39, 183.25]], 'area': 5163.523649999999, 'iscrowd': 0, 'image_id': 492642, 'bbox': [403.99, 99.96, 83.3, 83.29], 'category_id': 58, 'id': 1401}\n",
      "{'segmentation': [[284.98, 38.92, 168.03, 54.57, 158.14, 99.04, 167.2, 117.99, 157.32, 164.11, 158.97, 176.47, 203.44, 207.77, 330.29, 211.89, 450.54, 187.18, 461.25, 151.76, 459.6, 133.64, 434.07, 131.17, 443.13, 92.46, 434.89, 54.57, 417.59, 38.92]], 'area': 45435.3965, 'iscrowd': 0, 'image_id': 61215, 'bbox': [157.32, 38.92, 303.93, 172.97], 'category_id': 58, 'id': 1402}\n",
      "{'segmentation': [[304.17, 484.82, 294.54, 502.71, 289.03, 538.49, 311.05, 579.78, 382.62, 586.67, 406.02, 566.02, 456.95, 568.77, 444.56, 538.49, 418.41, 526.11, 389.51, 491.7, 355.1, 476.56]], 'area': 12187.073149999997, 'iscrowd': 0, 'image_id': 178208, 'bbox': [289.03, 476.56, 167.92, 110.11], 'category_id': 58, 'id': 1403}\n",
      "{'segmentation': [[405.85, 309.65, 391.19, 283.08, 384.77, 262.01, 385.69, 243.69, 418.67, 207.05, 442.49, 189.64, 456.23, 178.65, 486.46, 162.15, 521.28, 147.5, 532.27, 160.32, 549.68, 155.74, 563.42, 159.41, 569.83, 170.4, 570.75, 174.98, 585.41, 169.48, 602.81, 169.48, 609.23, 188.72, 610.14, 210.71, 604.65, 225.37, 595.48, 241.86, 581.74, 257.43, 563.42, 267.51, 552.43, 282.17, 539.6, 312.4, 531.35, 326.14, 516.7, 337.14, 495.63, 345.38, 463.56, 352.71, 440.66, 347.21, 440.66, 331.64, 428.75, 329.81, 415.92, 324.31, 407.68, 316.06, 404.01, 303.24]], 'area': 29652.344999999994, 'iscrowd': 0, 'image_id': 569667, 'bbox': [384.77, 147.5, 225.37, 205.21], 'category_id': 58, 'id': 1407}\n",
      "{'segmentation': [[279.74, 297.29, 272.52, 276.65, 283.87, 276.65, 289.03, 281.81, 292.13, 298.32, 291.1, 303.48, 284.9, 306.58, 278.71, 300.39]], 'area': 363.79755000000023, 'iscrowd': 0, 'image_id': 495875, 'bbox': [272.52, 276.65, 19.61, 29.93], 'category_id': 58, 'id': 1409}\n",
      "{'segmentation': [[289.65, 199.79, 279.19, 197.18, 278.54, 185.41, 289.0, 175.6, 291.61, 172.99, 321.03, 172.99, 413.21, 171.68, 419.1, 186.06, 419.1, 188.03, 387.06, 195.87, 351.11, 195.87, 304.69, 197.18, 287.04, 198.49]], 'area': 3115.0795499999995, 'iscrowd': 0, 'image_id': 235437, 'bbox': [278.54, 171.68, 140.56, 28.11], 'category_id': 58, 'id': 1414}\n",
      "{'segmentation': [[562.16, 229.24, 512.04, 279.36, 501.23, 306.88, 518.92, 330.47, 547.42, 335.38, 590.66, 301.96, 593.61, 290.17, 621.13, 262.65, 610.32, 241.03]], 'area': 7424.5738, 'iscrowd': 0, 'image_id': 339295, 'bbox': [501.23, 229.24, 119.9, 106.14], 'category_id': 58, 'id': 1420}\n",
      "{'segmentation': [[122.84, 334.71, 105.29, 348.13, 90.84, 368.77, 87.74, 389.42, 99.1, 393.55, 108.39, 378.06, 118.71, 358.45, 135.23, 334.71]], 'area': 1063.51635, 'iscrowd': 0, 'image_id': 528892, 'bbox': [87.74, 334.71, 47.49, 58.84], 'category_id': 58, 'id': 1428}\n",
      "{'segmentation': [[304.15, 166.05, 273.92, 188.95, 254.68, 226.51, 245.52, 281.48, 257.43, 307.13, 300.49, 317.21, 305.07, 299.8, 323.39, 325.45, 355.46, 323.62, 394.85, 291.56, 381.11, 250.33, 426.92, 199.95, 453.48, 179.79, 453.48, 171.55, 438.83, 161.47, 424.17, 157.8, 416.84, 159.64, 401.26, 135.82, 366.45, 133.98]], 'area': 24269.47, 'iscrowd': 0, 'image_id': 569667, 'bbox': [245.52, 133.98, 207.96, 191.47], 'category_id': 58, 'id': 1447}\n",
      "{'segmentation': [[225.03, 404.9, 199.23, 415.23, 197.16, 418.32, 211.61, 418.32, 235.35, 406.97]], 'area': 209.26585000000023, 'iscrowd': 0, 'image_id': 205955, 'bbox': [197.16, 404.9, 38.19, 13.42], 'category_id': 58, 'id': 1451}\n",
      "{'segmentation': [[294.32, 220.74, 290.64, 216.14, 286.96, 209.7, 288.8, 201.42, 298.0, 197.74, 389.97, 193.15, 408.36, 193.15, 419.4, 183.95, 424.0, 183.95, 431.36, 189.47, 432.28, 196.82, 434.12, 206.02, 424.0, 209.7, 360.54, 220.74, 312.71, 219.82, 292.48, 222.58]], 'area': 3360.0978499999997, 'iscrowd': 0, 'image_id': 235437, 'bbox': [286.96, 183.95, 147.16, 38.63], 'category_id': 58, 'id': 1453}\n"
     ]
    }
   ],
   "source": [
    "index = 100\n",
    "\n",
    "for i in range(index):\n",
    "    #print(train['images'][1])\n",
    "    print(train['annotations'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 抽取 coco 中的某一个类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/store2/dataset/coco/annotations/instances_train2014.json\n",
      "loading annotations into memory...\n",
      "Done (t=8.86s)\n",
      "creating index...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index created!\n",
      "{1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', 51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}\n",
      "[84]\n",
      "book 3734\n",
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   ],
   "source": [
    "from pycocotools.coco import COCO\n",
    "import os\n",
    "import shutil\n",
    "from tqdm import tqdm\n",
    "import skimage.io as io\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from PIL import Image, ImageDraw\n",
    "  \n",
    "#the path you want to save your results for coco to voc\n",
    "savepath=\"/store2/dataset/coco/yolo/\" #保存提取类的路径,我放在同一路径下\n",
    "img_dir=os.path.join(savepath, 'images')\n",
    "anno_dir=os.path.join(savepath, 'Annotations')\n",
    "# datasets_list=['train2014', 'val2014']\n",
    "datasets_list=['train2014']\n",
    "  \n",
    "classes_names = ['book'] #coco有80类，这里写要提取类的名字，以person为例\n",
    "#Store annotations and train2014/val2014/... in this folder\n",
    "dataDir= '/store2/dataset/coco' #原coco数据集\n",
    "  \n",
    "headstr = \"\"\"\\\n",
    "<annotation>\n",
    " <folder>VOC</folder>\n",
    " <filename>%s</filename>\n",
    " <source>\n",
    " <database>My Database</database>\n",
    " <annotation>COCO</annotation>\n",
    " <image>flickr</image>\n",
    " <flickrid>NULL</flickrid>\n",
    " </source>\n",
    " <owner>\n",
    " <flickrid>NULL</flickrid>\n",
    " <name>company</name>\n",
    " </owner>\n",
    " <size>\n",
    " <width>%d</width>\n",
    " <height>%d</height>\n",
    " <depth>%d</depth>\n",
    " </size>\n",
    " <segmented>0</segmented>\n",
    "\"\"\"\n",
    "objstr = \"\"\"\\\n",
    " <object>\n",
    " <name>%s</name>\n",
    " <pose>Unspecified</pose>\n",
    " <truncated>0</truncated>\n",
    " <difficult>0</difficult>\n",
    " <bndbox>\n",
    "  <xmin>%d</xmin>\n",
    "  <ymin>%d</ymin>\n",
    "  <xmax>%d</xmax>\n",
    "  <ymax>%d</ymax>\n",
    " </bndbox>\n",
    " </object>\n",
    "\"\"\"\n",
    "  \n",
    "tailstr = '''\\\n",
    "</annotation>\n",
    "'''\n",
    "  \n",
    "#if the dir is not exists,make it,else delete it\n",
    "def mkr(path):\n",
    "    if os.path.exists(path):\n",
    "        shutil.rmtree(path)\n",
    "        os.mkdir(path)\n",
    "    else:\n",
    "        os.mkdir(path)\n",
    "mkr(img_dir)\n",
    "mkr(anno_dir)\n",
    "\n",
    "def id2name(coco):\n",
    "    classes=dict()\n",
    "    for cls in coco.dataset['categories']:\n",
    "        classes[cls['id']]=cls['name']\n",
    "    return classes\n",
    "  \n",
    "def write_xml(anno_path,head, objs, tail):\n",
    "    f = open(anno_path, \"w\")\n",
    "    f.write(head)\n",
    "    for obj in objs:\n",
    "        f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4]))\n",
    "    f.write(tail)\n",
    "  \n",
    "  \n",
    "def save_annotations_and_imgs(coco,dataset,filename,objs):\n",
    "    #eg:COCO_train2014_000000196610.jpg --> COCO_train2014_000000196610.xml\n",
    "    anno_path=os.path.join(anno_dir, filename[:-3]+'xml')\n",
    "    img_path=os.path.join(dataDir, dataset, filename)\n",
    "    dst_imgpath=os.path.join(img_dir, filename)\n",
    "\n",
    "    img=cv2.imread(img_path)\n",
    "    #if (img.shape[2] == 1):\n",
    "    # print(filename + \" not a RGB image\")\n",
    "    # return\n",
    "    shutil.copy(img_path, dst_imgpath)\n",
    "\n",
    "    head= headstr % (filename, img.shape[1], img.shape[0], img.shape[2])\n",
    "    tail = tailstr\n",
    "    write_xml(anno_path,head, objs, tail)\n",
    "  \n",
    "  \n",
    "def showimg(coco,dataset,img,classes,cls_id,show=True):\n",
    "    global dataDir\n",
    "    I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))\n",
    "    #通过id，得到注释的信息\n",
    "    annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)\n",
    "    # print(annIds)\n",
    "    anns = coco.loadAnns(annIds)\n",
    "    # print(anns)\n",
    "    # coco.showAnns(anns)\n",
    "    objs = []\n",
    "    for ann in anns:\n",
    "        class_name=classes[ann['category_id']]\n",
    "    if class_name in classes_names:\n",
    "        print(class_name)\n",
    "    if 'bbox' in ann:\n",
    "        bbox=ann['bbox']\n",
    "        xmin = int(bbox[0])\n",
    "        ymin = int(bbox[1])\n",
    "        xmax = int(bbox[2] + bbox[0])\n",
    "        ymax = int(bbox[3] + bbox[1])\n",
    "        obj = [class_name, xmin, ymin, xmax, ymax]\n",
    "        objs.append(obj)\n",
    "        draw = ImageDraw.Draw(I)\n",
    "        draw.rectangle([xmin, ymin, xmax, ymax])\n",
    "    if show:\n",
    "        plt.figure()\n",
    "        plt.axis('off')\n",
    "        plt.imshow(I)\n",
    "        plt.show()\n",
    "\n",
    "    return objs\n",
    "  \n",
    "for dataset in datasets_list:\n",
    "    #./COCO/annotations/instances_train2014.json\n",
    "    annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)\n",
    "    print(annFile)\n",
    "    #COCO API for initializing annotated data\n",
    "    coco = COCO(annFile)\n",
    "\n",
    "    #show all classes in coco\n",
    "    classes = id2name(coco)\n",
    "    print(classes)\n",
    "    #[1, 2, 3, 4, 6, 8]\n",
    "    classes_ids = coco.getCatIds(catNms=classes_names)\n",
    "    print(classes_ids)\n",
    "    for cls in classes_names:\n",
    "    #Get ID number of this class\n",
    "        cls_id=coco.getCatIds(catNms=[cls])\n",
    "        img_ids=coco.getImgIds(catIds=cls_id)\n",
    "    print(cls,len(img_ids))\n",
    "    \n",
    "# imgIds=img_ids[0:10]\n",
    "for imgId in tqdm(img_ids):\n",
    "    img = coco.loadImgs(imgId)[0]\n",
    "    filename = img['file_name']\n",
    "    # print(filename)\n",
    "    objs=showimg(coco, dataset, img, classes,classes_ids,show=False)\n",
    "    #print(objs)\n",
    "    save_annotations_and_imgs(coco, dataset, filename, objs)"
   ]
  }
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